Founding Data Scientist Capacity and Pricing Optimization

You will build and own quantitative models that guide pricing, packaging, and capacity allocation. You will model supply and demand, design dynamic pricing policies, run experiments and causal studies, optimize GPU and capacity mixes, and ship production-quality models that support business decisions.

Responsibilities

• Build fill-rate models for serverless inference while balancing utilization, performance, and latency targets.
• Model traffic sharing and multiplexing across models and customers to establish safe capacity buffers.
• Design pricing for low-utilization capacity, including spot, preemptible, off-peak, overnight, and sub-scale offerings.
• Develop pricing policies that optimize capacity utilization and price yield.
• Design and execute experiments and causal studies to estimate elasticity.
• Optimize model-to-hardware matching across GPU generations, vendors, clouds, and regions.
• Optimize capacity across commitment types and terms while quantifying cost, flexibility, and utilization risk.

Requirements

• Master’s degree in Operations Research, Economics, Industrial Engineering, Statistics, or a related quantitative field with 7+ years of hands-on experience in pricing, revenue management, or optimization.
• Strong grounding in at least two of stochastic optimization, revenue management and pricing, causal inference and experimental design, or combinatorial optimization.
• Track record of delivering production-quality models with direct business impact.
• Strong coding skills and experience with optimization tooling and methodologies.
• Ability to communicate tradeoffs and uncertainty clearly to engineers and executives.